Prediction Model Pooling, Selection and Performance Evaluation Across Multiply Imputed Datasets

Pooling, backward and forward selection of linear, logistic and Cox regression models in multiply imputed datasets. Backward and forward selection can be done from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and the median p-values method. This is also possible for Mixed models. The models can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of predictors. The stability of the models can be evaluated using bootstrapping and cluster bootstrapping. The package further contains functions to pool the model performance as ROC/AUC, R-squares, scaled Brier score, H&L test and calibration plots for logistic regression models. Internal validation can be done with cross-validation or bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. Backward and forward selection as part of internal validation is possible. A function to externally validate logistic prediction models in multiple imputed datasets is available and a function to compare models. Eekhout (2017) . Wiel (2009) . Marshall (2009) .


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1.0.0 by Martijn Heymans, 4 months ago

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Authors: Martijn Heymans [cre, aut] , Iris Eekhout [ctb]

Documentation:   PDF Manual  

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GPL (>= 2) license

Imports ggplot2, norm, survival, mitools, pROC, rms, ResourceSelection, magrittr, rsample, mice, mitml, cvAUC, dplyr, purrr, tidyr, tibble, stringr, lme4, miceadds, car

Suggests foreign, knitr, rmarkdown, testthat, bookdown, readr

See at CRAN